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import torch
from seagull.utils import disable_torch_init
from transformers import AutoTokenizer, CLIPImageProcessor
from seagull.model.language_model.seagull_llama import SeagullLlamaForCausalLM
from seagull.mm_utils import tokenizer_image_token
from seagull.conversation import conv_templates, SeparatorStyle
from seagull.constants import IMAGE_TOKEN_INDEX
from seagull.train.train import DataArguments
from functools import partial
import os
import numpy as np
import cv2
from typing import List
from PIL import Image
from pycocotools import mask as mask_utils
class Seagull():
def __init__(self, model_path, device='cuda'):
disable_torch_init()
model_path = os.path.expanduser(model_path)
self.tokenizer = AutoTokenizer.from_pretrained(model_path, model_max_length=2048, padding_side="right", use_fast=True)
self.model = SeagullLlamaForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16,).to(device)
self.tokenizer.pad_token = self.tokenizer.unk_token
self.image_processor = CLIPImageProcessor(do_resize=True, size={"shortest_edge":512}, resample=3, do_center_crop=True, crop_size={"height": 512, "width": 512},
do_rescale=True, rescale_factor=0.00392156862745098, do_normalize=True, image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711], do_convert_rgb=True, )
spi_tokens = ['<global>', '<local>']
self.tokenizer.add_tokens(spi_tokens, special_tokens=True)
for m in self.model.modules():
m.tokenizer = self.tokenizer
vision_tower = self.model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
vision_tower.to(dtype=torch.float16, device=device)
begin_str = "<image>\nThis provides an overview of the image.\n Please answer the following questions about the provided region. Note: Distortions include: blur, colorfulness, compression, contrast exposure and noise.\n Here is the region <global><local>. "
instruction = {
'distortion': 'Provide the distortion type of this region.',
'quality': 'Analyze the quality of this region.',
'importance': 'Consider the impact of this region on the overall image quality. Analyze its importance to the overall image quality.'
}
self.ids_input = {}
for ins_type, ins in instruction.items():
conv = conv_templates['seagull_v1'].copy()
qs = begin_str + ins
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
self.ids_input[ins_type] = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.model.device)
self.stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
def init_image(self, img):
if isinstance(img, dict):
img = img['image']
elif isinstance(img, List):
img = cv2.imread(img[0])
img = img[:, :, ::-1]
h_, w_ = img.shape[:2]
if h_ > 512:
ratio = 512 / h_
new_h, new_w = int(h_ * ratio), int(w_ * ratio)
preprocessed_img = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
else:
preprocessed_img = img.copy()
return (preprocessed_img, preprocessed_img, preprocessed_img, preprocessed_img)
def preprocess(self, img):
image = self.image_processor.preprocess(img,
do_center_crop=False,
return_tensors='pt')['pixel_values'][0]
image = torch.nn.functional.interpolate(image.unsqueeze(0),
size=(512, 512),
mode='bilinear',
align_corners=False).squeeze(0)
return image
def seagull_predict(self, img, mask, instruct_type, mask_type='rle'):
if isinstance(img, str):
img = cv2.imread(img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
h, w, _ = img.shape
if mask_type == 'rle': # use the mask to indicate the roi
compressed_rle = {'size' : [h, w], 'counts' : mask}
mask = mask_utils.decode(compressed_rle)
elif mask_type == 'points': # use the point to indicate the roi
x_min, y_min, w1, h1 = mask
x_max, y_max = x_min + w1, y_min + h1
mask = np.zeros_like(img[:, :, 0])
mask[max(0, y_min):min(y_max, mask.shape[0]), max(0, x_min):min(x_max, mask.shape[1])] = 1
image = self.preprocess(img)
mask = np.array(mask, dtype=np.int)
ys, xs = np.where(mask > 0)
if len(xs) > 0 and len(ys) > 0:
x_min, x_max = np.min(xs), np.max(xs)
y_min, y_max = np.min(ys), np.max(ys)
w1 = x_max - x_min
h1 = y_max - y_min
bounding_box = (x_min, y_min, w1, h1)
else:
bounding_box = None
mask = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_NEAREST)
mask = np.array(mask > 0.1, dtype=np.uint8)
masks = torch.Tensor(mask).unsqueeze(0).to(self.model.device)
input_ids = self.ids_input[instruct_type.split()[0].lower()]
x1, y1, w1, h1 = list(map(int, bounding_box)) # x y w h
cropped_img = img[y1:y1 + h1, x1:x1 + w1]
cropped_img = Image.fromarray(cropped_img)
cropped_img = self.preprocess(cropped_img)
with torch.inference_mode():
self.model.orig_forward = self.model.forward
self.model.forward = partial(self.model.orig_forward,
img_metas=[None],
masks=[masks.half()],
cropped_img=cropped_img.unsqueeze(0)
)
output_ids = self.model.generate(
input_ids,
images=image.unsqueeze(0).half().to(self.model.device),
do_sample=False,
temperature=1,
max_new_tokens=2048,
use_cache=True,
num_beams=1,
top_k = 0,
top_p = 1,
)
self.model.forward = self.model.orig_forward
input_token_len = input_ids.shape[1]
n_diff_input_output = (
input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(
f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = self.tokenizer.batch_decode(output_ids[:, input_token_len:],
skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(self.stop_str):
outputs = outputs[:-len(self.stop_str)]
outputs = outputs.strip()
if ':' in outputs:
outputs = outputs.split(':')[1]
outputs_list = outputs.split('.')
outputs_list_final = []
outputs_str = ''
for output in outputs_list:
if output not in outputs_list_final:
if output=='':
continue
outputs_list_final.append(output)
outputs_str+=output+'.'
else:
break
return outputs_str |